Applications of deep learning solutions, which are usually trained with large amount of dataset, in controlling the spread of Coronavirus Disease 2019 (COVID-19) have shown promising results. Motivated by the lack of large number of well-annotated dataset during the onset of a novel disease, we present a high-performing, interpretable few-shot learning network that detects positive COVID-19 cases with limited examples of ultrasound images. Extensive experiments are conducted to evaluate model performance under different encoder architectures, number of training shots and classification problem complexity. When trained with only 5-shots, network classifies between positive and negative COVID-19 cases with 99.3% overall accuracy, 99.5% recall and 99.25% precision for positive cases. Network explainability is evaluated with two visual explanation tools and reviewed by a practicing clinician to ensure validity of network's decision-making process.